Obesity Pillars Roundtable: Body mass index and body composition in Black and Female individuals. Race-relevant or racist? Sex-relevant or sexist?
Bibliographic record
Abstract
Background: ) is the most common metric to diagnose overweight and obesity. However, a body composition analysis more thoroughly assesses adiposity, percent body fat, lean body mass (i.e., including skeletal muscle), and sometimes bone mineral density. BMI is not an accurate assessment of body fat in individuals with increased or decreased muscle mass; the diagnostic utility of BMI in individuals is also influenced by race and sex. Methods: Previous Obesity Pillars Roundtables addressed the diagnostic limitations of BMI, the importance of android and visceral fat (especially among those with South and East Asian ancestry), and considerations of obesity among individuals who identify as Hispanic, diverse in sexual-orientation, Black, Native American, and having ancestry from the Mediterranean and Middle East regions. This roundtable examines considerations of BMI in Black and female individuals. Results: The panelists agreed that body composition assessment was a more accurate measure of adiposity and muscle mass than BMI. When it came to matters of race and sex, one panelist felt: "race is a social construct and not a defining biology." Another felt that: "BMI should be a screening tool to prompt further evaluation of adiposity that utilizes better diagnostic tools for body composition." Regarding bias and misperceptions of resistance training in female individuals, another panelist stated: "I have spent my entire medical career taking care of women and have never seen a woman unintentionally gain 'too much' muscle mass and bulk up from moderate strength training." Conclusions: Conveying the importance of race and sex regarding body composition has proven challenging, with the discussion sometimes devolving into misunderstandings or misinformation that may be perceived as racist or sexist. Body composition analysis is the ultimate diagnostic equalizer in addressing the inaccuracies and biases inherent in the exclusive use of BMI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".